For parameter settings, we use a default of \(N=2\)  levels, with \(D_1=48\)  and \(D_2=24\)  hypothesis planes respectively. The minimum and maximum depth \(d_{min}\)  and \(d_{max}\)  for the first level is set to be \(0.2m\)  and \(8m\) . We use a default of \(M=3\)  synthesized views rendered at vertical baseline placements \(-0.24m, +0.24m, +0.4m\) . The loss weights \(\omega _1\)  and \(\omega _2\)  are set to 1 and \(0.02\) .
We train our framework from scratch using Adam optimizer (\(\beta _1=0.9, \beta _2=0.999\) ) with a batch size of 8. Initial learning rates for the first and the second stage are set to 0.0002 and 0.0005. We separately train the coarse network for 10 epochs and then train the entire framework end-to-end for 200 epochs.
Both of the learning rates decay by a factor of 0.5 every 30 epochs.
Performances are evaluated based on commonly used depth quality measures [1]}: absolute relative error (Abs Rel), square relative error (Sq Rel), linear root mean square error(RMSE) and its natural log scale (RMSE log) and inlier ratios (\(\delta _i < 1.25^i\) , \(i \in \lbrace 1, 2, 3\rbrace \) ).
